Method for judging the seriousness of a motor vehicle crash

ABSTRACT

Like in a conventional crash detection algorithm, a crash is detected if a threshold value of an acceleration or an integrated acceleration is exceeded. Various characteristic values for characterizing the sensor signal paths are collected and supplied to the neural network. Said neural network returns the characteristic values for the seriousness of the crash, thereby facilitating the various retention systems to reaction individually. The invention provides a method for reliably, simply and quickly making statements regarding the seriousness and the course of a motor vehicle crash and for controlling occupant protection systems according to demand.

BACKGROUND AND SUMMARY OF THE INVENTION

The invention relates to a method of detecting the seriousness of avehicle crash, in which the output signal of an acceleration sensor isprocessed and supplied to a neural network which controls a triggeringunit for an occupant protection system.

Such a method is known from U.S. Pat. No. 5,583,771 A. In this case, theoutput signal of a single acceleration sensor is stored for a definedtime period with respect to its path, and information, such as theamplitude, the course of the velocity, etc., is determined from thesignal path. This information is fed as input information into theneural network which decides whether a single air bag is ignited.

The known method has a large number of disadvantages. On the one hand,it is necessary to store the path of the crash signal for a predefinedtime period and, naturally, only analyze it subsequently. The use of asingle acceleration sensor does not permit judgment of all possiblevehicle crashes with respect to their seriousness with sufficientcertainty.

The cause is a normally existing directional dependence of accelerationsensors. If the acceleration sensor is capable, for example, ofdetecting a frontal crash, a side crash can, as a rule, not be detectedor can at least not be detected with the same precision. Such a singleacceleration sensor is, as a rule, arranged centrally in the vehicle. Asa result of the vehicle structure, the deceleration at the site of theacceleration sensor takes place only in a delayed manner and its path iscompletely different from the path taking place, for example, at theimpact site of an obstacle. As a rule, this results in considerableproblems with respect to detecting the seriousness of the vehicle crashwith sufficient precision. This sometimes results in the problem of notbeing able to detect the crash in sufficient time.

Since the neural network is naturally trained by means of precedingsignal paths for different types of crashes, experience has shown thatwhen looking at only a single sensor signal, no conclusion can be drawnon the further course of the vehicle crash if, as in U.S. Pat. No.5,583,771, only the previous path of the sensor signal is analyzed. Theknown method is therefore only suitable to a limited extent. It suppliesonly rough outlines and furnishes useful criteria only for the decisionon whether an occupant protection system should be triggered at all.

It is an object of the present invention to provide a method of theabove-mentioned type which supplies significantly better informationconcerning a vehicle crash.

The invention meets these needs by providing a method of detecting theseriousness of a vehicle crash, in which the output signal of anacceleration sensor is processed and supplied to a neural network whichcontrols a triggering unit for an occupant protection system. Fordetecting also the course of the vehicle crash, additional crash sensorsare provided, which supply a physical value identical with or similar tothe output signal of the acceleration sensor as input signals for theneural network. By means of the triggering unit, several occupantprotection systems are controlled corresponding to the seriousness andthe course of the vehicle crash.

Other objects, advantages and novel features of the present inventionwill become apparent from the following detailed description of theinvention when considered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of the apparatus for performing themethod according to the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to the FIGURE, the present invention includes an accelerationsensor 10 supplying to an output signal to a neural network control unit12. Also, additional crash sensors 14, 14′ are provided that likewisesupply an output signal to the neural network 12. The neural networkcontrol unit 12 is coupled to a trigger unit 16 which controls at leastone occupant protection system 18.

The use of a plurality of crash sensors permits not only an arrangementof these sensors centrally, but also at the sites which arepreferentially affected in the event of a vehicle crash. For a frontalcrash, these sites are in the area of the forward structure, forexample, on the engine mount; for a rear-end crash, these sites are inthe area of the rearward structure. A side impact can preferably bedetected by sensors which are arranged in the side area of the vehicle.Because of the large number of crash sensors, for example, a total ofeight or more, information on not only the seriousness, of the crash,but also concerning the course of a vehicle crash can be obtained.

The decentralized crash sensors and the central acceleration sensorsupply a physically equivalent output signal for the same point in time.This point in time is determined by a trigger signal which is emitted,for example, by one of the crash sensors or the acceleration sensor ifits output signal exceeds a predefined threshold value. In the case of avehicle crash, this will be the sensor which is closest to the impactsite of an object. This sensor is the one most affected out of all thesensors and causes the other sensors to supply their respective outputsignals at one and the same point in time. This point in time can, forexample, be selected to be 5 ms after the detection of a vehicle crash.

When applied to a large number of crash or acceleration sensors, the useof a neural network has the special advantage that it can still supplyinformation on the seriousness and the course of a vehicle crash even ifone or more sensors fail. By contrast, in the case of U.S. Pat. No.5,583,771 A, the failure of the single sensor makes it impossible tosupply any information at all concerning the vehicle crash.

The crash sensors supply a physical value having the same quality as thevalue supplied by the acceleration sensor. These values can also beacceleration values. In that case, the crash sensors would, for example,also be constructed in the same manner as the acceleration sensor andwould operate according to the same physical principle.

A further improvement of the information on the seriousness and thecourse of a vehicle crash can be achieved by integrating the outputsignal of the crash sensors and of the acceleration sensor over time.The first integration will then result in information concerning thespeed or the relative speed at the respective site of the sensor.

A clear improvement with respect to the informational value can beobtained when the output signals of the sensors are integrated twiceover time. The result is information on the path covered by the sensorsite. The processing of this information in the neural network takesplace rapidly and, even after a brief course of the vehicle crash,results in extensive information on the further course of the crash. Ifthe point in time at which information is gathered concerning thecovered path is chosen, for example, again to be equal to 5 ms after thedetection of the vehicle crash, sufficient information will be availableafter a few ms which, as required, permits the activation of differentoccupant protection devices or the preparation for the crash course tobe expected with high probability. In addition to the number of occupantprotection devices which are controlled, the intensity with which thesedevices are applied can also be defined by means of the neural network.

So far, only the case the input signals are fed at a defined point intime only once into the neural network has been considered. A furtherimprovement of the reliability of the information of whether and to whatextent occupant protection devices must be activated can be achieved.Here, the physical values formed from the output signals of theacceleration sensor and of the crash sensors are fed at successivedefined points in time as input signals into the neuronal network. Thisis advantageous in two respects.

On the one hand, it can be checked whether the “prediction” concerningthe further course of the crash, which was made by means of thepreceding input signals, is correct. If necessary, a correction of theactivating program of the occupant protection devices must take place.

On the other hand, influences can also be taken into account which areunusual and/or cannot be detected from the preceding input signals orcannot be detected sufficiently clearly. One example is the pole impact,where the obstacle is often detected late after it has penetrated farinto the vehicle.

The neural network according to the invention is not constantly queriedas in U.S. Pat. No. 5,583,771 A but, as in the case of a conventionalcrash detection algorithm, it is triggered first in the case of a crash,for example, by the fact that a filtered acceleration value exceeds acertain threshold. Subsequently, several characteristic values of theacceleration paths are transmitted by several sensors to the neuralnetwork as y-values (but not their entire time-related course). Thecharacteristic values of the acceleration paths are obtained by means oftime-window and/or time-related double integrals of the accelerationsand/or diverse mathematical combinations of the different characteristicsignal values. The number of required inputs into the neural network istherefore no longer several dozens to hundreds of inputs, but ratheronly a small number of inputs (less than 10).

The output signal of the neural network also does not lead to the directcontrolling of the air bags (FIRE/NOFIRE) but consists of a parameterwhich describes the “seriousness of the crash” (for example, x=(impactposition, impact velocity, . . . ). When reliable information concerningthe seriousness of the crash cannot yet be obtained, the neural networkcan be triggered by way of another triggering threshold, which is raisedwith respect to the first triggering threshold, and can be queriedagain.

By determining the seriousness of a crash, a modularity of the algorithmis permitted so that several units (several air bags, belt tighteningdevices, etc.) can react in a manner which is adapted to the seriousnessof the crash.

Because the neural network is called up only after being triggering andnot constantly, and also because the operation takes place by means ofmuch more highly processed data, the required expenditures can also beimplemented using the computing capacity already existing in currentconventional control units and processors. As a result of the greaterprocessing of the signal data, results from crash simulations can alsobe used as example data for training purposes.

The foregoing disclosure has been set forth merely illustrate theinvention and is not intended to be limiting. Since modifications of thedisclosed embodiments incorporating the spirit and substance of theinvention may occur to persons skilled in the art, the invention shouldbe construed to include everything within the scope of the appendedclaims and equivalents thereof.

What is claimed is:
 1. A method of detecting the severity of a vehiclecrash, the method comprising the acts of: providing a neural networkthat controls a triggering unit for an occupant protection system in amotor vehicle; generating an output signal from an acceleration sensorin the event of the vehicle crash; generating from additional crashsensors respective output signals representing physical values identicalwith or similar to the output signal of the acceleration sensor; whereinone of the sensors causes the other of the sensors to supply theirrespective output signals to the neural network at a same point in timein response to a trigger signal generated by the one of the sensors whenthe output signal of the one of the sensors exceeds a predefinedthreshold value; determining a severity and a course of the vehiclecrash by the neural network based on the output signals supplied to theneural network; and controlling a plurality of occupant protectionsystems, via the triggering unit, in accordance with the severity andthe course of the vehicle crash determined by the neural network.
 2. Themethod according to claim 1, wherein said additional crash sensorssupply information concerning respective relative speeds at therespective sensor sites on the vehicle.
 3. The method according to claim2, wherein said additional crash sensors are acceleration sensors. 4.The method according claim 2, further comprising the act of integratingover time the output signals of the additional crash sensors and theacceleration sensor.
 5. The method according to claim 2, furthercomprising the act of integrating twice over time the output signals ofthe additional crash sensors.
 6. The method according to claim 2,further comprising the act of feeding at successive defined points intime the physical values formed from the output signals of theacceleration sensor and the additional crash sensors as input signalsinto the neural network.
 7. The method according to claim 1, wherein theadditional crash sensors supply information concerning respectiverelative displacements of the sites at which the sensors are arranged onthe vehicle.
 8. The method according to claim 7, wherein said additionalcrash sensors are acceleration sensors.
 9. The method according claim 7,further comprising the act of integrating over time the output signalsof the additional crash sensors and the acceleration sensor.
 10. Themethod according to claim 7, further comprising the act of integratingtwice over time the output signals of the additional crash sensors. 11.The method according to claim 7, further comprising the act of feedingat successive defined points in time the physical values formed from theoutput signals of the acceleration sensor and the additional crashsensors as input signals into the neural network.
 12. The methodaccording to claim 1, wherein said additional crash sensors areacceleration sensors.
 13. The method according claim 12, furthercomprising the act of integrating over time the output signals of theadditional crash sensors and the acceleration sensor.
 14. The methodaccording to claim 12, further comprising the act of integrating twiceover time the output signals of the additional crash sensors.
 15. Themethod according to claim 12, further comprising the act of feeding atsuccessive defined points in time the physical values formed from theoutput signals of the acceleration sensor and the additional crashsensors as input signals into the neural network.
 16. The methodaccording claim 1, further comprising the act of integrating over timethe output signals of the additional crash sensors and the accelerationsensor.
 17. The method according to claim 16, further comprising the actof integrating twice over time the output signals of the additionalcrash sensors.
 18. The method according to claim 16, further comprisingthe act of feeding at successive defined points in time the physicalvalues formed from the output signals of the acceleration sensor and theadditional crash sensors as input signals into the neural network. 19.The method according to claim 1, further comprising the act ofintegrating twice over time the output signals of the additional crashsensors.
 20. The method according to claim 19, further comprising theact of feeding at successive defined points in time the physical valuesformed from the output signals of the acceleration sensor and theadditional crash sensors as input signals into the neural network. 21.The method according to claim 1, further comprising the act of feedingat successive defined points in time the physical values formed from theoutput signals of the acceleration sensor and the additional crashsensors as input signals into the neural network.